Triple
T23843931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duffy’s Cliff |
E591066
|
entity |
| Predicate | playingImpact |
P118563
|
FINISHED |
| Object | could turn routine fly balls into difficult plays |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: could turn routine fly balls into difficult plays | Statement: [Duffy’s Cliff, playingImpact, could turn routine fly balls into difficult plays]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playingImpact Context triple: [Duffy’s Cliff, playingImpact, could turn routine fly balls into difficult plays]
-
A.
gameplayImpact
chosen
Indicates how one element in a game affects the mechanics, difficulty, or overall experience of playing that game.
-
B.
impactOnField
Indicates the effect or influence that one entity, action, or development has on a particular field or domain.
-
C.
seasonImpact
Indicates how a particular season influences or affects another entity, condition, or outcome.
-
D.
encodingImpact
Indicates how one encoding or encoding choice affects, modifies, or constrains another process, representation, or outcome.
-
E.
exportImpact
Indicates the effect or consequences that an entity’s exports have on another entity, system, or context.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e25d1de32c8190a907afe9c3d6cd6d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c88a4b948190989a261e79b996a6 |
completed | April 29, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f1614612b481908c45d99e588882f9 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:09 p.m.